Association Rule Mining (ARM), also referred as Frequent Set Mining (FSM), is a data-mining technique that identifies strong and interesting relations between variables in datasets using different measures of interestingness. ARM has been a key module of many recommendation so systems and has created many commercial opportunities for on-line retail stores. In the past ten years, this technique has also been widely used in web usage mining, traffic accident analysis, intrusion detection, market basket analysis, bioinformatics, etc.
As modern datasets continue to grow rapidly, the execution efficiency of ARM becomes a bottleneck for its application in new domains. Many previous studies have been devoted to improving the performance of sequential CPU-based ARM implementation. Different data structures were proposed to include horizontal representation, vertical representation, and matrix representation (Document 1). Multiple renowned algorithms have been developed including Apriori (Document 2), Eclat (Document 3), and FP-growth (Document 4). A number of parallel acceleration based solutions have also been developed on multi-core CPU (Document 5), GPU (Document 6), and FPGA (Document 7).
Recently, Micron proposed a novel and powerful non-von Neumann architecture—the Automata Processor (AP). The AP architecture demonstrates a massively parallel computing ability through a huge number of state elements. It also achieves fine-grained communication ability through its configurable routing mechanism. These advantages make the AP suitable for pattern-matching centered tasks like ARM. Very recently, the AP has been successfully used to accelerate the tasks of regular expression matching (Document 8) and DNA motif searching (Document 9).